Reproducing JPM’s US Rates Strategy RV Trade Idea

Note - Treasuries: Is this it? (12 July 2024)

100:95 weighted 0.625% Jul-26s / 1.25% Dec-26s steepeners

Turning to relative value, we see opportunities in the 2- to 2.5-year sector, where original-issue 5-year notes have recently cheapened relative to our par curve. In particular, we like fading the cheapening of 0.625% Jul-26s against 1.25% Dec-26s. As Figure 8 shows, this curve appears 4bp too flat relative to the shape of 2s/5s. Against this backdrop, we recommend 100:95 weighted 0.625% Jul-26s / 1.25% Dec-26s steepeners (see Trade recommendations)

Separately, last month we highlighted that the 2036-38 sector had cheapened significantly and had recommended fading the cheapening in 4.75% Feb-37s, against 4.5% Aug-39s, which were about to drop out of the US basket (see Treasuries, US Fixed Income Markets Weekly, 6/14/24). Since then, Feb-37s/Aug-39s curve has normalized somewhat relative to the shape of 10s/20s (Figure 9). Traditionally, we’d wait for further mean-reversion in this relationship, before taking profits. However, as Figure 10.Howevr, thlaionsp betwnhs curvehas wknedigfcantly shows, the relationship between these two curves has deteriorated materially in recent weeks; i.e. the shape of Feb-37/Aug39 is being driven by more idiosyncratic factors. Against this backdrop, we recommend unwinding this trade at a profit (see Trade recommendations).

import sys
sys.path.append("../../")

from CurveInterpolator import GeneralCurveInterpolator
from CurveDataFetcher import CurveDataFetcher
from utils.rv_utils import cusip_spread_rv_regression
from utils.viz import plot_usts, run_rolling_regression_df
from models.calibrate import calibrate_mles_ols, calibrate_nss_ols
from models.NelsonSiegelSvensson import NelsonSiegelSvenssonCurve

import pandas as pd
import numpy as np
import scipy
from datetime import datetime
from typing import Dict, List
import tqdm

import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
params = {
    "axes.titlesize": "x-large",
    "legend.fontsize": "x-large",
    "axes.labelsize": "x-large",
    "xtick.labelsize": "x-large",
    "ytick.labelsize": "x-large",
}
pylab.rcParams.update(params)

import seaborn as sns
sns.set(style="whitegrid", palette="dark")

import nest_asyncio
nest_asyncio.apply()

import warnings
warnings.filterwarnings('ignore', category=FutureWarning)
warnings.filterwarnings('ignore', category=pd.errors.SettingWithCopyWarning)
warnings.filterwarnings('ignore', category=RuntimeWarning)

import plotly
plotly.offline.init_notebook_mode()

%load_ext autoreload
%autoreload 2

The autoreload extension is already loaded. To reload it, use:
  %reload_ext autoreload

curve_data_fetcher = CurveDataFetcher(use_ust_issue_date=True)

quote_type = "eod"
as_of_date = datetime(2024, 7, 12)

curve_set_df = curve_data_fetcher.build_curve_set(
    as_of_date=as_of_date,
    sorted=True,
    include_off_the_run_number=True,
    market_cols_to_return=[f"{quote_type}_price", f"{quote_type}_yield"],
    calc_free_float=True,
    use_github=True,
)

curve_set_df

	cusip	security_type	auction_date	issue_date	maturity_date	time_to_maturity	int_rate	high_investment_rate	is_on_the_run	ust_label	...	parValue	percentOutstanding	est_outstanding_amt	corpus_cusip	outstanding_amt	portion_unstripped_amt	portion_stripped_amt	reconstituted_amt	free_float	rank
0	912797KP1	Bill	2024-06-13	2024-06-18	2024-07-16	0.010959	NaN	5.355	False	5.355% Jul-24	...	5.989944e+08	0.002844	2.105814e+11	NaN	0.000000e+00	NaN	0.000000e+00	NaN	-598.9944	16.0
1	912797KQ9	Bill	2024-06-20	2024-06-25	2024-07-23	0.030137	NaN	5.324	False	5.324% Jul-24	...	6.528583e+08	0.003099	2.106357e+11	NaN	0.000000e+00	NaN	0.000000e+00	NaN	-652.8583	15.0
2	912797KR7	Bill	2024-06-27	2024-07-02	2024-07-30	0.049315	NaN	5.365	False	5.365% Jul-24	...	7.225868e+08	0.003429	2.107041e+11	NaN	0.000000e+00	NaN	0.000000e+00	NaN	-722.5868	14.0
3	912797KW6	Bill	2024-07-03	2024-07-09	2024-08-06	0.068493	NaN	5.375	True	5.375% Aug-24	...	7.354958e+08	0.003409	2.157435e+11	NaN	0.000000e+00	NaN	0.000000e+00	NaN	-735.4958	13.0
4	912797KX4	Bill	2024-06-13	2024-06-18	2024-08-13	0.087671	NaN	5.382	False	5.382% Aug-24	...	5.267986e+08	0.004036	1.305234e+11	NaN	0.000000e+00	NaN	0.000000e+00	NaN	-526.7986	12.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
387	912810TR9	Bond	2023-07-13	2023-07-17	2053-05-15	28.860274	3.625	NaN	False	3.625% May-53	...	5.723292e+09	0.091437	6.259307e+10	912803GS6	6.259307e+10	39689851.8	2.290322e+10	1283290.0	33966.5599	4.0
388	912810TT5	Bond	2023-10-12	2023-10-16	2053-08-15	29.112329	4.125	NaN	False	4.125% Aug-53	...	8.606596e+09	0.120247	7.157430e+10	912803GU1	7.157430e+10	61748876.0	9.825425e+09	108683.0	53142.2798	3.0
389	912810TV0	Bond	2024-01-11	2024-01-16	2053-11-15	29.364384	4.750	NaN	False	4.750% Nov-53	...	4.567153e+08	0.006874	6.644364e+10	912803GW7	6.644364e+10	58598705.9	7.844939e+09	1837270.0	58141.9906	2.0
390	912810TX6	Bond	2024-04-11	2024-04-15	2054-02-15	29.616438	4.250	NaN	False	4.250% Feb-54	...	2.211754e+09	0.031064	7.119879e+10	912803GY3	7.119879e+10	61136104.7	1.006269e+10	3279100.0	58924.3502	1.0
391	912810UA4	Bond	2024-06-13	2024-06-17	2054-05-15	29.860274	4.625	NaN	True	4.625% May-54	...	3.853850e+09	0.075789	5.085006e+10	912803HB2	5.085006e+10	47473108.6	3.376951e+09	479600.0	43619.2587	0.0

392 rows × 25 columns

Build Par Curve Model

def liquidity_premium_curve_set_filter(curve_set_df: pd.DataFrame):

    # remove OTRs, olds, double olds, triple olds
    curve_set_filtered_df = curve_set_df[
        (curve_set_df["rank"] != 0) & (curve_set_df["rank"] != 1) & (curve_set_df["rank"] != 2) & (curve_set_df["rank"] != 3)
    ]

    # remove TBills
    curve_set_filtered_df = curve_set_filtered_df[curve_set_filtered_df["security_type"] != "Bill"]

    # remove low free float bonds (< $5bn)
    curve_set_filtered_df = curve_set_filtered_df[curve_set_filtered_df["free_float"] > 5000]

    # filter out bonds very close to maturity
    curve_set_filtered_df = curve_set_filtered_df[curve_set_filtered_df["time_to_maturity"] > 30 / 360]

    # remove CTDs
    curve_set_filtered_df = curve_set_filtered_df[
        ~curve_set_filtered_df["cusip"].isin(
            [
                curve_data_fetcher.ust_data_fetcher.cme_ust_label_to_cusip("4.625s 2026-09-15")["cusip"],  # TU
                curve_data_fetcher.ust_data_fetcher.cme_ust_label_to_cusip("4.125s 2027-09-30")["cusip"],  # Z3N
                curve_data_fetcher.ust_data_fetcher.cme_ust_label_to_cusip("4.25s 2029-02-28")["cusip"],  # FV
                curve_data_fetcher.ust_data_fetcher.cme_ust_label_to_cusip("4.25s 2031-06-30")["cusip"],  # TY
                curve_data_fetcher.ust_data_fetcher.cme_ust_label_to_cusip("4.375s 2034-05-15")["cusip"],  # TN
                curve_data_fetcher.ust_data_fetcher.cme_ust_label_to_cusip("4.625s 2040-02-15")["cusip"],  # US
                curve_data_fetcher.ust_data_fetcher.cme_ust_label_to_cusip("4.5s 2044-02-15")["cusip"],  # TWE
                curve_data_fetcher.ust_data_fetcher.cme_ust_label_to_cusip("4.75s 2053-11-15")["cusip"],  # UL
            ]
        )
    ]

    curve_set_filtered_df = curve_set_filtered_df.sort_values(by=["time_to_maturity"])

    return curve_set_filtered_df


def no_filter(curve_set_df: pd.DataFrame):
    return curve_set_df

# filter and fit bspline w/ knots are liquidity points
curve_set_filtered_df = liquidity_premium_curve_set_filter(curve_set_df=curve_set_df)

filtered_fitted_interpolator = GeneralCurveInterpolator(
    x=curve_set_filtered_df["time_to_maturity"].to_numpy(),
    y=curve_set_filtered_df[f"{quote_type}_yield"].to_numpy(),
)

fitted_bspline = filtered_fitted_interpolator.b_spline_with_knots_interpolation(
    knots=[0.5, 1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 15, 20, 25],
    k=3,
    return_func=True,
)

nss_func, status_nss, _ = calibrate_nss_ols(
    curve_set_filtered_df["time_to_maturity"].to_numpy(),
    curve_set_filtered_df[f"{quote_type}_yield"].to_numpy(),
)
assert status_nss

mles_func, status_mles = calibrate_mles_ols(
    curve_set_filtered_df["time_to_maturity"].to_numpy(),
    curve_set_filtered_df[f"{quote_type}_yield"].to_numpy(),
    overnight_rate=5.31,
    N=9,
)

plot_usts(
    curve_set_df=curve_set_df,
    ttm_col="time_to_maturity",
    ytm_col=f"{quote_type}_yield",
    hover_data=[
        "issue_date",
        "maturity_date",
        "cusip",
        "original_security_term",
        "ust_label",
        f"{quote_type}_price",
        "free_float",
    ],
    ust_labels_highlighter=[
        ("0.625% Jul-26", "red"), ("1.250% Dec-26", "blue"), 
    ],
    zero_curves=[(fitted_bspline, "BSpline k=3 - Zero Filtered Fit"), (nss_func, "Nelson Siegel Svensson"), (mles_func, "Merrill Lynch Exponential Spline")],
    title=f"All USTs - using {f"{quote_type}_yield"} - as of {as_of_date}",
    y_axis_range=[4.0, 5.5]
)

Fetching historical curve sets to regress 0.625% Jul-26s / 1.25% Dec-26s vs our fitted model over time

start_date = datetime(2024, 4, 1)
end_date = datetime(2024, 7, 12)

curve_sets_dict_df, fitted_curves_dict = curve_data_fetcher.fetch_historical_curve_sets(
    start_date=start_date,
    end_date=end_date,
    fetch_soma_holdings=True,
    fetch_stripping_data=True,
    calc_free_float=True,
    fitted_curves=[
        ("LPF", f"{quote_type}_yield", liquidity_premium_curve_set_filter),
    ],
)

FETCHING CURVE SETS...: 100%|██████████| 94/94 [00:02<00:00, 40.08it/s]
AGGREGATING CURVE SET DFs: 100%|██████████| 94/94 [00:02<00:00, 36.61it/s]

cusip_timeseries: Dict[str, List[Dict[str, str | float | int]]] = {}
fitted_cubic_spline_timeseries: Dict[datetime, scipy.interpolate] = {}
fitted_bspline_timeseries: Dict[datetime, scipy.interpolate] = {}
fitted_smooth_spline_timeseries: Dict[datetime, scipy.interpolate] = {}

for dt in tqdm.tqdm(curve_sets_dict_df.keys(), desc="Main Loop"):
    fitted_cubic_spline = fitted_curves_dict[dt]["LPF"].b_spline_with_knots_interpolation(
        knots=[0.5, 1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 15, 20, 25],
        k=3,
        return_func=True,
    )
    fitted_bspline = fitted_curves_dict[dt]["LPF"].b_spline_with_knots_interpolation(
        knots=[0.5, 1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 15, 20, 25], k=5, return_func=True
    )
    fitted_smooth_spline = fitted_curves_dict[dt]["LPF"].b_spline_with_knots_interpolation(
        knots=[0.5, 1, 2, 3, 5, 7, 10, 20], k=4, return_func=True
    )

    fitted_cubic_spline_timeseries[dt] = fitted_cubic_spline
    fitted_bspline_timeseries[dt] = fitted_bspline
    fitted_smooth_spline_timeseries[dt] = fitted_smooth_spline 

    curr_curve_set_df = curve_sets_dict_df[dt]
    curr_curve_set_df["lpf_cubic_spline_spread"] = curr_curve_set_df["eod_yield"] - fitted_cubic_spline(curr_curve_set_df["time_to_maturity"])
    curr_curve_set_df["lpf_bspline_spread"] = curr_curve_set_df["eod_yield"] - fitted_bspline(curr_curve_set_df["time_to_maturity"])
    curr_curve_set_df["lpf_smooth_spline_spread"] = curr_curve_set_df["eod_yield"] - fitted_smooth_spline(curr_curve_set_df["time_to_maturity"])

    for _, row in curr_curve_set_df.iterrows():
        if row["cusip"] not in cusip_timeseries:
            cusip_timeseries[row["cusip"]] = []

        payload = {
            "Date": dt,
            "cusip": row["cusip"],
            f"{quote_type}_yield": row[f"{quote_type}_yield"],
            f"{quote_type}_price": row[f"{quote_type}_price"],
            "lpf_cubic_spline_spread": row["lpf_cubic_spline_spread"],
            "lpf_bspline_spread": row["lpf_bspline_spread"],
            "lpf_smooth_spline_spread": row["lpf_smooth_spline_spread"],
            "free_float": row["free_float"],
            "est_outstanding_amount": row["est_outstanding_amt"],
            "soma_holdings": row["parValue"],
            "soma_holdings_percent_outstanding": row["percentOutstanding"],
            "stripped_amount": row["portion_stripped_amt"],
            "reconstituted_amount": row["reconstituted_amt"],
            "lpf_cubic_spline": fitted_cubic_spline,
            "lpf_bspline": fitted_bspline,
            "lpf_smooth_spline": fitted_smooth_spline,
        }
        
        cusip_timeseries[row["cusip"]].append(payload)

Main Loop: 100%|██████████| 72/72 [00:01<00:00, 42.67it/s]

ct_yields_df = curve_data_fetcher.fedinvest_data_fetcher.get_historical_ct_yields(start_date=start_date, end_date=end_date)
ct_yields_df

	Date	CT2M	CT3M	CT6M	CT1	CT2	CT3	CT5	CT7	CT10	CT20	CT30
0	2024-04-01	NaN	5.382196	5.393189	5.078389	4.715645	4.511302	4.335726	4.334039	4.324568	4.576638	4.468567
1	2024-04-02	NaN	5.374033	5.382022	5.067000	4.699117	4.511451	4.349917	4.365707	4.360544	4.615595	4.505884
2	2024-04-03	NaN	5.384197	5.370860	5.044716	4.682556	4.488627	4.335827	4.355191	4.352618	4.615593	4.509835
3	2024-04-04	NaN	5.373238	5.348941	5.022444	4.649279	4.465782	4.300537	4.307803	4.308815	4.574195	4.468574
4	2024-04-05	NaN	5.382595	5.369280	5.064586	4.733240	4.546828	4.378591	4.392378	4.388936	4.647449	4.541568
...	...	...	...	...	...	...	...	...	...	...	...	...
67	2024-07-08	NaN	5.412165	5.339352	5.019376	4.623836	4.405211	4.228497	4.228815	4.271883	4.566545	4.458725
68	2024-07-09	NaN	5.433883	5.349724	5.040739	4.623696	4.404947	4.242501	4.249656	4.295275	4.592975	4.488691
69	2024-07-10	NaN	5.433481	5.349334	5.029463	4.623557	4.404683	4.242449	4.244394	4.279618	4.576118	4.471789
70	2024-07-11	NaN	5.401046	5.254785	4.909030	4.506034	4.278732	4.130070	4.145396	4.197901	4.504451	4.406717
71	2024-07-12	NaN	5.389180	5.242893	4.875006	4.454775	4.231934	4.101737	4.124423	4.178382	4.497274	4.393776

72 rows × 12 columns

label1 = "0.625% Jul-26" 
label2 = "1.250% Dec-26" 

cusip_spread_rv_regression(
    curve_data_fetcher=curve_data_fetcher,
    label1=label1,
    label2=label2,
    cusip_timeseries=cusip_timeseries,
    fitted_splines_timeseries_dict={
        "lpf_cubic_spline": fitted_cubic_spline_timeseries,
        "lpf_bspline": fitted_bspline_timeseries,
        "lpf_smooth_spline": fitted_smooth_spline_timeseries,
    },
    benchmark_tenor_1=2,
    benchmark_tenor_2=5,
    ct_yields_df=ct_yields_df 
)

lpf_cubic_spline is Benchmark Spline

                                  OLS Regression Results                                 
=========================================================================================
Dep. Variable:     0.625% Jul-26 / 1.250% Dec-26   R-squared:                       0.547
Model:                                       OLS   Adj. R-squared:                  0.541
Method:                            Least Squares   F-statistic:                     84.65
Date:                           Sun, 06 Oct 2024   Prob (F-statistic):           1.14e-13
Time:                                   19:20:31   Log-Likelihood:                 207.35
No. Observations:                             72   AIC:                            -410.7
Df Residuals:                                 70   BIC:                            -406.1
Df Model:                                      1                                         
Covariance Type:                       nonrobust                                         
=========================================================================================
                            coef    std err          t      P>|t|      [0.025      0.975]
-----------------------------------------------------------------------------------------
const                     0.0267      0.014      1.890      0.063      -0.001       0.055
lpf_cubic_spline_2s5s     0.3671      0.040      9.200      0.000       0.288       0.447
==============================================================================
Omnibus:                       12.099   Durbin-Watson:                   0.732
Prob(Omnibus):                  0.002   Jarque-Bera (JB):               13.589
Skew:                          -0.807   Prob(JB):                      0.00112
Kurtosis:                       4.387   Cond. No.                         27.6
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

                                  OLS Regression Results                                 
=========================================================================================
Dep. Variable:     0.625% Jul-26 / 1.250% Dec-26   R-squared:                       0.616
Model:                                       OLS   Adj. R-squared:                  0.611
Method:                            Least Squares   F-statistic:                     112.3
Date:                           Sun, 06 Oct 2024   Prob (F-statistic):           3.38e-16
Time:                                   19:20:32   Log-Likelihood:                 213.28
No. Observations:                             72   AIC:                            -422.6
Df Residuals:                                 70   BIC:                            -418.0
Df Model:                                      1                                         
Covariance Type:                       nonrobust                                         
==============================================================================
                 coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
const          0.0165      0.011      1.461      0.148      -0.006       0.039
CT2-CT5        0.3182      0.030     10.598      0.000       0.258       0.378
==============================================================================
Omnibus:                       12.181   Durbin-Watson:                   0.873
Prob(Omnibus):                  0.002   Jarque-Bera (JB):               14.298
Skew:                          -0.778   Prob(JB):                     0.000785
Kurtosis:                       4.531   Cond. No.                         22.9
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

Using lpf_cubic_spline for UST Metrics Calcs

0.625% Jul-26 Metrics Calc: 72it [00:01, 36.30it/s]
1.250% Dec-26 Metrics Calc: 72it [00:01, 37.27it/s]

Revisting 100:98 weighted 4.75% Feb 37s / 4.5% Aug 39s steepeners

start_date = datetime(2023, 6, 1)
end_date = datetime(2024, 7, 12)

curve_sets_dict_df, fitted_curves_dict = curve_data_fetcher.fetch_historical_curve_sets(
    start_date=start_date,
    end_date=end_date,
    fetch_soma_holdings=True,
    fetch_stripping_data=True,
    calc_free_float=True,
    fitted_curves=[
        ("LPF", f"{quote_type}_yield", liquidity_premium_curve_set_filter),
    ],
)

cusip_timeseries: Dict[str, List[Dict[str, str | float | int]]] = {}
fitted_cubic_spline_timeseries: Dict[datetime, scipy.interpolate] = {}
fitted_bspline_timeseries: Dict[datetime, scipy.interpolate] = {}
fitted_smooth_spline_timeseries: Dict[datetime, scipy.interpolate] = {}

for dt in tqdm.tqdm(curve_sets_dict_df.keys(), desc="Main Loop"):
    fitted_cubic_spline = fitted_curves_dict[dt]["LPF"].b_spline_with_knots_interpolation(
        knots=[0.5, 1, 2, 3, 4, 5, 7, 10, 15, 20, 25],
        k=3,
        return_func=True,
    )
    fitted_bspline = fitted_curves_dict[dt]["LPF"].b_spline_with_knots_interpolation(
        knots=[0.5, 1, 2, 3, 4, 5, 7, 10, 15, 20, 25], k=5, return_func=True
    )
    fitted_smooth_spline = fitted_curves_dict[dt]["LPF"].b_spline_with_knots_interpolation(
        knots=[0.5, 1, 2, 3, 5, 7, 10, 20], k=4, return_func=True
    )

    fitted_cubic_spline_timeseries[dt] = fitted_cubic_spline
    fitted_bspline_timeseries[dt] = fitted_bspline
    fitted_smooth_spline_timeseries[dt] = fitted_smooth_spline 

    curr_curve_set_df = curve_sets_dict_df[dt]
    curr_curve_set_df["lpf_cubic_spline_spread"] = curr_curve_set_df["eod_yield"] - fitted_cubic_spline(curr_curve_set_df["time_to_maturity"])
    curr_curve_set_df["lpf_bspline_spread"] = curr_curve_set_df["eod_yield"] - fitted_bspline(curr_curve_set_df["time_to_maturity"])
    curr_curve_set_df["lpf_smooth_spline_spread"] = curr_curve_set_df["eod_yield"] - fitted_smooth_spline(curr_curve_set_df["time_to_maturity"])

    for _, row in curr_curve_set_df.iterrows():
        if row["cusip"] not in cusip_timeseries:
            cusip_timeseries[row["cusip"]] = []

        payload = {
            "Date": dt,
            "cusip": row["cusip"],
            f"{quote_type}_yield": row[f"{quote_type}_yield"],
            f"{quote_type}_price": row[f"{quote_type}_price"],
            "lpf_cubic_spline_spread": row["lpf_cubic_spline_spread"],
            "lpf_bspline_spread": row["lpf_bspline_spread"],
            "lpf_smooth_spline_spread": row["lpf_smooth_spline_spread"],
            "free_float": row["free_float"],
            "est_outstanding_amount": row["est_outstanding_amt"],
            "soma_holdings": row["parValue"],
            "soma_holdings_percent_outstanding": row["percentOutstanding"],
            "stripped_amount": row["portion_stripped_amt"],
            "reconstituted_amount": row["reconstituted_amt"],
            "lpf_cubic_spline": fitted_cubic_spline,
            "lpf_bspline": fitted_bspline,
            "lpf_smooth_spline": fitted_smooth_spline,
        }
        
        cusip_timeseries[row["cusip"]].append(payload)
        

ct_yields_df = curve_data_fetcher.fedinvest_data_fetcher.get_historical_ct_yields(start_date=start_date, end_date=end_date)
ct_yields_df

FETCHING CURVE SETS...: 100%|██████████| 354/354 [00:06<00:00, 55.96it/s]
AGGREGATING CURVE SET DFs: 100%|██████████| 354/354 [00:10<00:00, 33.42it/s]
Main Loop: 100%|██████████| 277/277 [00:07<00:00, 37.66it/s]

	Date	CT2M	CT3M	CT6M	CT1	CT2	CT3	CT5	CT7	CT10	CT20	CT30
0	2023-06-01	NaN	5.454437	5.446241	5.135337	4.332358	3.986357	3.700936	3.658065	3.608150	3.984667	3.834714
1	2023-06-02	NaN	5.463895	5.531196	5.254383	4.498651	4.135988	3.840122	3.775501	3.688603	4.035661	3.878399
2	2023-06-05	NaN	5.452812	5.498458	5.210113	4.465482	4.113408	3.819236	3.765211	3.684818	4.035662	3.887563
3	2023-06-06	NaN	5.414579	5.465740	5.231540	4.515761	4.159771	3.847250	3.775460	3.692558	4.026355	3.867443
4	2023-06-07	NaN	5.392936	5.454566	5.198239	4.566230	4.217822	3.931477	3.878616	3.789183	4.122509	3.944831
...	...	...	...	...	...	...	...	...	...	...	...	...
272	2024-07-08	NaN	5.412165	5.339352	5.019376	4.623836	4.405211	4.228497	4.228815	4.271883	4.566545	4.458725
273	2024-07-09	NaN	5.433883	5.349724	5.040739	4.623696	4.404947	4.242501	4.249656	4.295275	4.592975	4.488691
274	2024-07-10	NaN	5.433481	5.349334	5.029463	4.623557	4.404683	4.242449	4.244394	4.279618	4.576118	4.471789
275	2024-07-11	NaN	5.401046	5.254785	4.909030	4.506034	4.278732	4.130070	4.145396	4.197901	4.504451	4.406717
276	2024-07-12	NaN	5.389180	5.242893	4.875006	4.454775	4.231934	4.101737	4.124423	4.178382	4.497274	4.393776

277 rows × 12 columns

label1 = "4.750% Feb-37" 
label2 = "4.500% Aug-39" 

cusip_spread_rv_regression(
    curve_data_fetcher=curve_data_fetcher,
    label1=label1,
    label2=label2,
    cusip_timeseries=cusip_timeseries,
    fitted_splines_timeseries_dict={
        "lpf_cubic_spline": fitted_cubic_spline_timeseries,
        "lpf_bspline": fitted_bspline_timeseries,
        "lpf_smooth_spline": fitted_smooth_spline_timeseries,
    },
    benchmark_tenor_1=10,
    benchmark_tenor_2=20,
    ct_yields_df=ct_yields_df,
    date_subset=(datetime(2024, 1, 1), datetime(2024, 7, 12))
)

lpf_cubic_spline is Benchmark Spline

                                  OLS Regression Results                                 
=========================================================================================
Dep. Variable:     4.750% Feb-37 / 4.500% Aug-39   R-squared:                       0.536
Model:                                       OLS   Adj. R-squared:                  0.533
Method:                            Least Squares   F-statistic:                     152.5
Date:                           Sun, 06 Oct 2024   Prob (F-statistic):           9.17e-24
Time:                                   20:04:50   Log-Likelihood:                 331.39
No. Observations:                            134   AIC:                            -658.8
Df Residuals:                                132   BIC:                            -653.0
Df Model:                                      1                                         
Covariance Type:                       nonrobust                                         
===========================================================================================
                              coef    std err          t      P>|t|      [0.025      0.975]
-------------------------------------------------------------------------------------------
const                      -0.0381      0.016     -2.451      0.016      -0.069      -0.007
lpf_cubic_spline_10s20s     0.4668      0.038     12.349      0.000       0.392       0.542
==============================================================================
Omnibus:                        6.384   Durbin-Watson:                   0.089
Prob(Omnibus):                  0.041   Jarque-Bera (JB):                6.104
Skew:                          -0.468   Prob(JB):                       0.0473
Kurtosis:                       2.532   Cond. No.                         24.8
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

                                  OLS Regression Results                                 
=========================================================================================
Dep. Variable:     4.750% Feb-37 / 4.500% Aug-39   R-squared:                       0.207
Model:                                       OLS   Adj. R-squared:                  0.201
Method:                            Least Squares   F-statistic:                     34.56
Date:                           Sun, 06 Oct 2024   Prob (F-statistic):           3.20e-08
Time:                                   20:04:51   Log-Likelihood:                 295.52
No. Observations:                            134   AIC:                            -587.0
Df Residuals:                                132   BIC:                            -581.3
Df Model:                                      1                                         
Covariance Type:                       nonrobust                                         
==============================================================================
                 coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
const          0.0574      0.016      3.507      0.001       0.025       0.090
CT10-CT20      0.3559      0.061      5.879      0.000       0.236       0.476
==============================================================================
Omnibus:                       18.832   Durbin-Watson:                   0.045
Prob(Omnibus):                  0.000   Jarque-Bera (JB):               23.560
Skew:                          -1.023   Prob(JB):                     7.66e-06
Kurtosis:                       2.821   Cond. No.                         27.9
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

Using lpf_cubic_spline for UST Metrics Calcs

4.750% Feb-37 Metrics Calc: 134it [00:04, 30.85it/s]
4.500% Aug-39 Metrics Calc: 134it [00:03, 36.47it/s]

benchmark_tenor_1 = 10
benchmark_tenor_2 = 20

cusip1 = curve_data_fetcher.ust_data_fetcher.ust_label_to_cusip(label1)["cusip"]
cusip2 = curve_data_fetcher.ust_data_fetcher.ust_label_to_cusip(label2)["cusip"]
cusip1_df = pd.DataFrame(cusip_timeseries[cusip1]).sort_values(by=["Date"])
cusip2_df = pd.DataFrame(cusip_timeseries[cusip2]).sort_values(by=["Date"])
spread_df = pd.DataFrame({"Date": cusip1_df["Date"], f"{label1} / {label2}": cusip2_df["eod_yield"] - cusip1_df["eod_yield"]})
ct_yields_df[f"CT{benchmark_tenor_1}-CT{benchmark_tenor_2}"] = ct_yields_df[f"CT{benchmark_tenor_2}"] - ct_yields_df[f"CT{benchmark_tenor_1}"]
spread_df = pd.merge(left=spread_df[["Date", f"{label1} / {label2}"]], right=ct_yields_df, on="Date", how="inner")

rolling_r2 = run_rolling_regression_df(
    df=spread_df,
    x_col=f"CT{benchmark_tenor_1}-CT{benchmark_tenor_2}",
    y_col=f"{label1} / {label2}",
    window=120
)